| A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications | |
Wang, Kunfeng1 ; Liu, Yuqiang1,2; Gou, Chao1,3; Wang, Fei-Yue1,4; Wang, Kunfeng(王坤峰)
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| Source Publication | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
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| 2016-06-01 | |
| Volume | 65Issue:6Pages:4144-4158 |
| Subtype | Article |
| Abstract | This paper proposes an effective multi-view learning approach to foreground detection for traffic surveillance applications. This approach involves three main steps. First, a reference background image is generated via temporal median filtering, and multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation, each of which represents a unique view) are extracted from the video sequence. Then, a multi-view learning strategy is devised to online estimate the conditional probability densities for both the foreground and the background. The probability densities of three features are approximately conditionally independent and are estimated with kernel density estimation. Pixel soft labeling is conducted by using Bayes rule, and the pixelwise foreground posteriors are computed. Finally, a Markov random field is constructed to incorporate the spatiotemporal context into the foreground/background decision model. The belief propagation algorithm is used to label each pixel of the current frame. Experimental results verify that the proposed approach is effective to detect foreground objects from challenging traffic environments and outperforms some state-of-the-art methods. |
| Keyword | Conditional Independence Foreground Detection Heterogeneous Features Markov Random Field (Mrf) Multi-view Learning |
| WOS Headings | Science & Technology ; Technology |
| Subject Area | Civil Engineering |
| DOI | 10.1109/TVT.2015.2509465 |
| WOS Keyword | GAUSSIAN MIXTURE MODEL ; REAL-TIME TRACKING ; BACKGROUND SUBTRACTION ; OBJECT DETECTION ; VISUAL SURVEILLANCE ; CAST SHADOWS ; SEGMENTATION ; SUPPRESSION ; EDGE |
| URL | 查看原文 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Organization | National Natural Science Foundation of China(61304200) ; MIIT Project of Internet of Things Development Fund(1F15E02) |
| WOS Research Area | Engineering ; Telecommunications ; Transportation |
| WOS Subject | Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology |
| WOS ID | WOS:000380068500026 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/10859 |
| Collection | 复杂系统管理与控制国家重点实验室_先进控制与自动化 |
| Corresponding Author | Wang, Kunfeng(王坤峰) |
| Affiliation | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.China Acad Railway Sci, Beijing 100081, Peoples R China 3.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 4.Natl Univ Def Technol, Res Ctr Computat Expt & Parallel Syst, Changsha 410073, Hunan, Peoples R China |
| Recommended Citation GB/T 7714 | Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,et al. A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2016,65(6):4144-4158. |
| APA | Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,Wang, Fei-Yue,&Wang, Kunfeng.(2016).A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,65(6),4144-4158. |
| MLA | Wang, Kunfeng,et al."A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 65.6(2016):4144-4158. |
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| 2016A Multi-View Lea（1153KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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